LENGTH-BIASED SEMICOMPETING RISKS MODELS FOR CROSS-SECTIONAL DATA: AN APPLICATION TO CURRENT DURATION OF PREGNANCY ATTEMPT DATA

• 成果类型: Article
• 署名作者: McLain, Alexander C.; Guo, Siyuan; Thoma, Marie; Zhang, Jiajia
• 署名单位: University of South Carolina System; University of South Carolina Columbia; University System of Maryland; University of Maryland College Park
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1428
• 发表日期: 2021
• 页码: 1054-1067
• 关键词: PROPORTIONAL HAZARDS MODEL; competing risks; survival analysis; estimating time; infertility; samples

摘要

Cross-sectional length-biased data arise from questions on the at-risk time for an event of interest from those who are at risk but have yet to experience the event. For example, in the National Survey on Family Growth (NSFG) women, who were currently attempting to become pregnant, were asked how long they had been attempting pregnancy. Cross-sectional survival analysis methods use the observed at-risk times to make inference on the distribution of the unobserved time-to-failure. However, methodological gaps in these methods remain such as how to handle semicompeting risks. For example, if the women attempting pregnancy had undergone fertility treatment during their current pregnancy attempt. In this paper we develop statistical methods that extend cross-sectional survival analysis methods to incorporate semicompeting risks. They can be used to estimate the distribution of the length of natural pregnancy attempts (i.e., without fertility treatment) while correctly accounting for women that sought fertility treatment prior to being sampled using cross-sectional data. We demonstrate our approach based on simulated data and an analysis of data from the NSFG. The proposed method results in separate survival curves for time-to-natural-pregnancy, time-to-fertility treatment and time-to-pregnancy after fertility treatment.